Biomedical Text Mining and Ontologies

Graph analytics for phenome-genome associations inference

The paper develops a graph-based framework to infer direct associations between HPO and GO terms, improving the interpretation of molecular processes in disease contexts.

Towards reliable named entity recognition in the biomedical domain

The paper evaluates methods to improve generalization of deep learning models for biomedical named entity recognition (BioNER) and introduces an open-source tool for the task.

CoCoScore: Context-aware co-occurrence scoring for text mining applications using distant supervision

CoCoScore improves relation extraction by scoring co-occurrence with contextual awareness, outperforming existing methods in disease-gene, tissue-gene, and protein-protein association identification.

variant2literature: full text literature search for genetic variants

variant2literature searches biomedical literature for genetic variants by processing images and normalizing variant formats to improve recall.

PathoPhenoDB: linking human pathogens to their disease phenotypes in support of infectious disease research

PathoPhenoDB links human pathogens to disease phenotypes to support infectious disease research by integrating pathogen-disease associations and drug resistance information.

Completing missing MeSH code mappings in UMLS through alternative expert-curated sources

This paper addresses gaps in UMLS disease mappings by using alternative expert-curated sources, resolving over 50% of missing mappings for some vocabularies.

PPPred: Classifying Protein-phenotype Co-mentions Extracted from Biomedical Literature

PPPred classifies protein-phenotype co-mentions from biomedical text using supervised learning, outperforming baselines in validation.

Similarity corpus on microbial transcriptional regulation

The paper creates a similarity corpus for microbial transcriptional regulation sentences, evaluated by curators to aid in biomedical literature curation.

CancerMine: A literature-mined resource for drivers, oncogenes and tumor suppressors in cancer

CancerMine compiles text-mined cancer gene annotations and provides a resource for gene-centric cancer type clustering validated against TCGA data.

Recapitulation and Retrospective Prediction of Biomedical Associations Using Temporally-enabled Word Embeddings

This paper uses temporally-enabled word embeddings to recapitulate and predict disease-gene associations from OMIM data, achieving high AUC and predicting some associations before their first literature mention.

Semantic Integration of Clinical Laboratory Tests from Electronic Health Records for Deep Phenotyping and Biomarker Discovery

The paper semantically integrates LOINC-encoded laboratory data with the Human Phenotype Ontology to identify asthma biomarkers from EHR data.

Consensus Development Of A Modern Ontology Of Emergency Department Presenting Problems: The HierArchical Presenting Problem OntologY (HaPPy)

The paper develops a standardized ontology for emergency department presenting problems using real-world data and expert consensus, achieving high validation accuracy.

PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

The paper introduces PGxO and PGxLOD to reconcile pharmacogenomic knowledge from diverse sources, enabling comparison of gene-drug-adverse event relationships.

Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies

The paper evaluates ontology design patterns for combining pathology and anatomy ontologies using an aging mice dataset, showing how different patterns perform in analysis tasks.

k-BOOM: A Bayesian approach to ontology structure inference, with applications in disease ontology construction

The paper introduces k-BOOM, a Bayesian method for merging ontologies by estimating logical relationships from mappings, applied to build a unified disease ontology.

Biomedical Concept Recognition Using Deep Neural Sequence Models

The paper improves biomedical concept recognition using deep neural sequence models, achieving higher F1 scores for ontology concept identification in scientific texts.

BO-LSTM: Classifying relations via long short-term memory networks along biomedical ontologies

BO-LSTM uses biomedical ontologies with LSTM networks to improve drug-drug interaction classification in text, achieving better performance with limited annotations.

Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language

The paper develops workflows to re-curate and enrich knowledge graphs encoded in Biological Expression Language using automated and manual methods.

Harnessing formal concepts of biological mechanism to analyze human disease

This paper introduces a formal framework for representing biological mechanisms and applies it to analyze human genetic disease mechanisms.

ClinPhen extracts and prioritizes patientphenotypes directly from medical records to accelerate genetic disease diagnosis

ClinPhen automatically extracts and prioritizes patient phenotypes from medical records to improve genetic disease diagnosis efficiency.

Biochat: a database for natural language processing of Gene Expression Omnibus data

Biochat links GEO datasets via metadata similarity to support multi-omics data integration through a web application.

Phenotero: annotate as you write

Phenotero integrates ontology annotation into writing workflows by leveraging Zotero to reference ontology classes in clinical reports and manuscripts.

Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning

This paper introduces a system using deep reinforcement learning to automatically curate a genetic association database by simulating human research behaviors.

Word and sentence embedding tools to measure semantic similarity of Gene Ontology terms by their definitions

The paper introduces two NLP-based methods to measure semantic similarity of Gene Ontology terms using their definitions, achieving better classification accuracy than tree-based methods.

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

The paper introduces a multi-task learning framework for biomedical named entity recognition that improves performance by sharing information across entity types.

Identifying high-priority proteins across the human diseasome using semantic similarity

The paper identifies high-priority proteins across the human diseasome using semantic similarity and provides a systematic collection of popular proteins across diseases, phenotypes, and pathways.

Automated literature mining and hypothesis generation through a network of Medical Subject Headings

This paper introduces MeTeOR, a method using MeSH terms to generate hypotheses from PubMed literature, showing improved reliability over existing methods and validating a new EGFR-CDK2 association.

MCO: towards an ontology and unified vocabulary for a framework-based annotation of microbial growth conditions

The paper introduces MCO, an ontology for standardized annotation of microbial growth conditions, enabling comparison of gene expression data across experiments.

The Structural Extraction of Chinese Medical Narratives

The paper compares a rule-based model and a CRF-based model for extracting structured information from Chinese medical narratives, showing both achieve high precision.

Identifying Genes in Published Pathway Figure Images

The paper identifies genes in published pathway figures using OCR and pathway modeling, finding 29,189 gene symbols across 3982 figures, significantly expanding gene associations beyond existing databases.